This short article deals with the implementation and evaluation of the effectiveness of a STEM learning programme for women. The one-week programme consists of workshops on various topics in the field of new technologies, industry 4.0, sustainability or study orientation. One of the assessment methods was the Word Association Test (WAT). The test is used to assess students' knowledge before and after the intervention by means of a pre-test and a post-test. The number of terms associated with the nine key words increased by approximately 27% overall in the post-test. This indicates

positive changes in the knowledge structures. However, it can be observed that the rate of change for the individual keywords is between -3% and 76%, so that different variables are discussed for each keyword.

The 13 Fundamental Learning Objectives (FLOs) from 2002/2005 is the most cited taxonomy for Learning Objectives in the undergraduate engineering laboratory. However, as early as 2003 a revision was suggested. A previous analysis of the objectives using the SOLO-Taxonomy as framework showed significant shortcomings regarding incorrect language, comprehensibility as well as some FLOs only addressing surface learning. Based on both works, this contribution presents a revised version of the 13 FLOs in which overlapping sub-objectives were cut, complex wording and ambiguous terms and operators (verbs describing what students are supposed to do) were improved. Furthermore, every FLO was reformulated to facilitate deep learning. The new FLOs were validated communicatively with researches an to ensure comprehensibility and the continuity of the fundamental engineering content. Issues of reformulating the FLOs to fit the framework of the SOLO-Taxonomy are also discussed. An initial A communicative validation with student assistants in which they compared the two versions of the FLOs, suggested that the reformulated FLOs were clearer to understand, making it easier for students to assess what is expected and what their assessment will be based on. Thus, the reformulated FLOs can not only be directly employed in engineering laboratories but can also be used as a stepping stone with the SOLO-Taxonomy to formulate more specific learning objectives tailored to a certain laboratory.

This research investigates how secondary school students engage with robotics activities within their technology and engineering education programs. The study focuses on project-based experiential learning, involving design, construction, and programming of robotic systems. The sample includes four groups of 10th and 11th graders from two high schools: high-achieving students from a comprehensive school (School A) and newcomer students from a boarding school (School B).

The researchers aimed to identify and compare learning engagement (LE) features typical for each group, using the engagement structures methodology adapted from mathematics education. This methodology examines students’ immediate desires and behaviors during learning activities, identifying patterns of engagement or disengagement.

Observations were conducted over an academic year, and engagement structures were identified and quantified based on their frequency among students. Qualitative explanations for these behaviors were derived from post-course questionnaires.

The study found that engagement structures related to excitement and interest in activities were prominent across all groups. However, the desire to impress classmates was more common among students from School A than School B. Additionally, a sense of obligation to follow teacher instructions was more frequent among 10th graders than 11th graders in School A.

The research highlights that learning engagement in robotics varies with curricula, learning environments, and student backgrounds. The identified engagement structures can help educators better understand and foster student engagement in robotics education.

Next year’s one robust grow (around 17% per annum) of IoT sector is expected, this growth is fueled by an increase in connected assets and corresponding investments in AI and cybersecurity.

The sensor market has grown significantly in recent years. A primary factor contributing to this growth is the increasing integration of sensors in consumer electronics, industrial applications, and automotive. The future of innovation, technology, and industrial advancement is significantly influenced by engineering education.

Engineering schools, like “Transylvania” University with Centre for Valorization and Transfer of Competences CVTC, will continue to mold the next generation of engineers, greatly enhancing society and technology because CVTC recognized like sustaining collaboration between academia and industry. This paper promotes and sustains all these ideas by building and test new IoT flexible monitoring systems for students training and involvement in future research activities.

This work aims to show that learning science, mathematics, and technology in engineering is deeply interconnected and synergistic. The Integrative Learning theoretical framework is used to show that a teaching approach that encourages the understanding of this relationship, helps students appreciate the relevance of their studies and encourages them to engage in interdisciplinary projects that combine scientific experimentation, mathematical reasoning, and the use of technological tools. While mathematics provides the foundational skills necessary for understanding scientific concepts and offers tools for modeling, analyzing data, and interpreting results crucial in scientific research, science relies heavily on mathematical principles. Whether in physics, chemistry, or biology, quantitative methods, statistical and numerical analyses, and other mathematical tools are essential for designing experiments, making predictions, and validating hypotheses. On the other hand, technology serves as a bridge between engineering, science and mathematics. It allows for the practical application of mathematical concepts and scientific theories. Many technological advancements arise from scientific discoveries and mathematical modeling. In other words, it is shown that the learning of science, mathematics, and technology is interdependent, and by using an interdisciplinary and integrative approach to learning students' knowledge is enriched, equipping them with essential skills for the future. This interconnected approach fosters a holistic, deeper comprehension of the world and encourages innovative and critical thinking in students.

This study introduces a RAG-based intelligent tutoring system enhanced to process multimodal data for higher education, addressing the limitations of traditional text-based LLM tutoring tools. While the previous version of the system focused on text responses, feedback from other International Confer-ences has highlighted the need for integrating additional data types like images and videos to foster a richer and more interactive learning experience. The pro-posed system combines LangChain, Large Vision Language Models (LVLMs), and the BridgeTower embedding model, creating unified representations of text, image, and video content. Educational resources are stored in high-dimensional VectorStores, enabling efficient retrieval and contextually relevant responses to students’ diverse queries. By expanding the Intelligent Agent’s capabilities to interpret visual content, this system aims to improve engagement, comprehension, and retention of complex academic material. Early testing has shown encouraging results in text processing, and similar success is anticipated with multimodal integration. This research demonstrates the potential for RAG-based multimodal tutoring to significantly enhance learning in higher education and recommends continued development of these capabilities for broader educational applications.